def plot_gyro(self):
N = self.raw_us.shape[0]
raw_us = self.raw_us[:, :3]
net_us = self.net_us[:, :3]
net_qs, imu_Rots, net_Rots = self.integrate_with_quaternions_superfast(N,
raw_us, net_us)
imu_rpys = 180/np.pi*SO3.to_rpy(imu_Rots).cpu()
net_rpys = 180/np.pi*SO3.to_rpy(net_Rots).cpu()
self.plot_orientation(imu_rpys, net_rpys, N)
self.plot_orientation_error(imu_Rots, net_Rots, N)
def align_traj(self):
"""yaw only and position alignment at initial time"""
self.gt['rpys'] = SO3.to_rpy(self.gt['Rots'].cuda()).cpu()
self.iekf['rpys'] = SO3.to_rpy(self.iekf['Rots'].cuda()).cpu()
self.gt['ps'] -= self.gt['ps'][0].clone()
self.iekf['ps'] -= self.iekf['ps'][0].clone()
rpys = self.gt['rpys'][:2] - self.iekf['rpys'][:2]
Rot = SO3.from_rpy(rpys[:, 0], rpys[:, 1], rpys[:, 2])
Rot = Rot[0].repeat(self.iekf['ps'].shape[0], 1, 1)
self.iekf['Rots'] = Rot.bmm(self.iekf['Rots'])
self.iekf['vs'] = bmv(Rot, self.iekf['vs'])
self.iekf['ps'] = bmv(Rot, self.iekf['ps'])
self.iekf['rpys'] = SO3.to_rpy(self.iekf['Rots'].cuda()).cpu()
def display_test(self, dataset, mode):
self.roes = {
'Rots': [],
'yaws': [],
}
self.to_open_vins(dataset)
for i, seq in enumerate(dataset.sequences):
print('\n', 'Results for sequence ' + seq )
self.seq = seq
# get ground truth
self.gt = dataset.load_gt(i)
Rots = SO3.from_quaternion(self.gt['qs'].cuda())
self.gt['Rots'] = Rots.cpu()
self.gt['rpys'] = SO3.to_rpy(Rots).cpu()
# get data and estimate
self.net_us = pload(self.address, seq, 'results.p')['hat_xs']
self.raw_us, _ = dataset[i]
N = self.net_us.shape[0]
self.gyro_corrections = (self.raw_us[:, :3] - self.net_us[:N, :3])
self.ts = torch.linspace(0, N*self.dt, N)
self.convert()
self.plot_gyro()
self.plot_gyro_correction()
plt.show()
def read_data(self, data_dir):
r"""Read the data from the dataset"""
# threshold for ZUPT ground truth
sm_velocity_max_threshold = 0.004 # m/s
f = os.path.join(self.predata_dir, 'urban06.p')
if True and os.path.exists(f):
return
print("Start read_data, be patient please")
def set_path(seq):
path_imu = os.path.join(data_dir, seq, "sensor_data",
"xsens_imu.csv")
path_gt = os.path.join(data_dir, seq, "global_pose.csv")
# path_odo = os.path.join(data_dir, seq, "encoder.csv")
return path_imu, path_gt
time_factor = 1e9 # ns -> s
def interpolate(x, t, t_int, angle=False):
"""
Interpolate ground truth with sensors
"""
x_int = np.zeros((t_int.shape[0], x.shape[1]))
for i in range(x.shape[1]):
if angle:
x[:, i] = np.unwrap(x[:, i])
x_int[:, i] = np.interp(t_int, t, x[:, i])
return x_int
sequences = os.listdir(data_dir)
# read each sequence
for sequence in sequences:
print("\nSequence name: " + sequence)
path_imu, path_gt = set_path(sequence)
imu = np.genfromtxt(path_imu, delimiter=",")
# Urban00-05 and campus00 have only quaternion and Euler data
if not imu.shape[1] > 10:
cprint("No IMU data for dataset " + sequence, 'yellow')
continue
gt = np.genfromtxt(path_gt, delimiter=",")
# time synchronization between IMU and ground truth
t0 = np.max([gt[0, 0], imu[0, 0]])
t_end = np.min([gt[-1, 0], imu[-1, 0]])
# start index
idx0_imu = np.searchsorted(imu[:, 0], t0)
idx0_gt = np.searchsorted(gt[:, 0], t0)
# end index
idx_end_imu = np.searchsorted(imu[:, 0], t_end, 'right')
idx_end_gt = np.searchsorted(gt[:, 0], t_end, 'right')
# subsample
imu = imu[idx0_imu:idx_end_imu]
gt = gt[idx0_gt:idx_end_gt]
t = imu[:, 0]
# take ground truth position
p_gt = gt[:, [4, 8, 12]]
p_gt = p_gt - p_gt[0]
# take ground matrix pose
Rot_gt = torch.Tensor(gt.shape[0], 3, 3)
for j in range(3):
Rot_gt[:, j] = torch.Tensor(gt[:, 1 + 4 * j:1 + 4 * j + 3])
q_gt = SO3.to_quaternion(Rot_gt)
# convert to angle orientation
rpys = SO3.to_rpy(Rot_gt)
t_gt = gt[:, 0]
# interpolate ground-truth
p_gt = interpolate(p_gt, t_gt, t)
rpys = interpolate(rpys.numpy(), t_gt, t, angle=True)
# convert from numpy
ts = (t - t0) / time_factor
p_gt = torch.Tensor(p_gt)
rpys = torch.Tensor(rpys).float()
q_gt = SO3.to_quaternion(
SO3.from_rpy(rpys[:, 0], rpys[:, 1], rpys[:, 2]))
imu = torch.Tensor(imu).float()
# take IMU gyro and accelerometer and magnetometer
imu = imu[:, 8:17]
dt = ts[1:] - ts[:-1]
# compute speed ground truth (apply smoothing)
v_gt = torch.zeros(p_gt.shape[0], 3)
for j in range(3):
p_gt_smooth = savgol_filter(p_gt[:, j], 11, 1)
v_j = (p_gt_smooth[1:] - p_gt_smooth[:-1]) / dt
v_j_smooth = savgol_filter(v_j, 11, 0)
v_gt[1:, j] = torch.Tensor(v_j_smooth)
# ground truth specific motion measurement (binary)
zupts = v_gt.norm(dim=1, keepdim=True) < sm_velocity_max_threshold
zupts = zupts.float()
# set ground truth consistent with ZUPT
v_gt[zupts.squeeze() == 1] = 0
# save for all training
mondict = {
'xs': zupts.float(),
'us': imu.float(),
}
pdump(mondict, self.predata_dir, sequence + ".p")
# save ground truth
mondict = {
'ts': ts,
'qs': q_gt.float(),
'vs': v_gt.float(),
'ps': p_gt.float(),
}
pdump(mondict, self.predata_dir, sequence + "_gt.p")